'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase					={
    "configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverOnnxConfig"],
    "tokenization_perceiver": ["PerceiverTokenizer"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["PerceiverFeatureExtractor"]
  __UpperCAmelCase					=["PerceiverImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "PerceiverForImageClassificationConvProcessing",
      "PerceiverForImageClassificationFourier",
      "PerceiverForImageClassificationLearned",
      "PerceiverForMaskedLM",
      "PerceiverForMultimodalAutoencoding",
      "PerceiverForOpticalFlow",
      "PerceiverForSequenceClassification",
      "PerceiverLayer",
      "PerceiverModel",
      "PerceiverPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
  from .tokenization_perceiver import PerceiverTokenizer

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_perceiver import PerceiverFeatureExtractor
    from .image_processing_perceiver import PerceiverImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_perceiver import (
        PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PerceiverForImageClassificationConvProcessing,
        PerceiverForImageClassificationFourier,
        PerceiverForImageClassificationLearned,
        PerceiverForMaskedLM,
        PerceiverForMultimodalAutoencoding,
        PerceiverForOpticalFlow,
        PerceiverForSequenceClassification,
        PerceiverLayer,
        PerceiverModel,
        PerceiverPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations(UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item							) for item in array							)

    return count_of_possible_combinations(UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase__							,    UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        __lowerCamelCase            =    sum(
            count_of_possible_combinations_with_dp_array(target - item							,    UpperCamelCase__							)
            for item in array							)
        __lowerCamelCase            =    answer
        return answer

    __lowerCamelCase            =    [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase__							,    UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    [0] * (target + 1)
    __lowerCamelCase            =    1

    for i in range(1							,    target + 1							):
        for j in range(UpperCamelCase__							):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __UpperCAmelCase					=3
  __UpperCAmelCase					=5
  __UpperCAmelCase					=[1, 2, 5]
  print(combination_sum_iv(n, array, target))
